Generally underutilized in the credit risk area. Particularly useful for testing metrics where there is little consensus on standard error or when statistical testing procedures are absent. Very often, even the inverted p-values of confidence intervals are sufficiently informative.
Advantages:
Disadvantages:
Testing Hypothesis:
What is the probability that PSI value is less or equal to 0.15?
Visualization:
## p-value = 21.26%
Dataset:
## Rating Grade # obs. DR
## 1 01 (-Inf,0.0199) 202 0.01
## 2 02 [0.0199,0.0263) 54 0.02
## 3 03 [0.0263,0.0369) 96 0.03
## 4 04 [0.0369,0.0903) 204 0.06
## 5 05 [0.0903,0.15) 103 0.11
## 6 06 [0.15,0.197) 41 0.12
## 7 07 [0.197,Inf) 50 0.32
Testing Hypothesis:
What is the probability that HHI value is greater or equal to 0.20?
Visualization:
## p-value = 23.72%
Dataset:
## Bootstrapped AUC summary:
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.6769 0.7414 0.7526 0.7525 0.7638 0.8067
## Development sample AUC 79%.
## Application portfolio AUC 75.2%.
Testing Hypothesis:
What is the probability that the application portfolio AUC is equal to 79%?
Visualization:
## 2*min(c(left-side p-value, right-side p-value))
## p-value = 2.06%